Vehicle passability obtaining method and device, terminal device and storage medium

Abstract

The application is suitable for the field of simulation test technology, and provides a vehicle passability obtaining method and device, terminal equipment and storage medium, the method comprises the following steps: constructing a simulation environment of a road condition in a driving video of a first vehicle according to the driving video of the first vehicle, then obtaining vehicle parameters of a second vehicle input or selected by a user, and constructing a vehicle simulation model of the second vehicle according to the vehicle parameters of the second vehicle, obtaining a simulation result of the second vehicle driving in the simulation environment according to the simulation environment and the vehicle simulation model; the simulation environment is constructed by the driving video of the first vehicle, the environment in the driving video can be reproduced, the second vehicle drives in the simulation environment, the running condition of the second vehicle in the simulation environment can be tested, whether the second vehicle can pass the road condition in the driving video of the first vehicle is determined, and whether the second vehicle can drive in the road condition of the first vehicle is determined for the user.

Description

Technical Field

[0001] This application belongs to the field of simulation testing technology, and in particular relates to a method, device, terminal equipment and storage medium for obtaining vehicle passability. Background Technology

[0002] To showcase vehicle performance, car manufacturers, or car enthusiasts, often demonstrate their driving skills by traversing challenging terrains. For example, off-road enthusiasts frequently drive on steep slopes, mountain roads, or through ditches. During these challenging drives, they often film the process to share their vehicle's capabilities or driving skills with a wider audience.

[0003] After seeing the vehicle driving videos, users often want to drive their own vehicles to challenge the road conditions shown in the videos. However, because their own vehicle model is different from the model of the vehicle in the driving video, users cannot determine whether their own vehicle can pass through the road conditions shown in the video. Summary of the Invention

[0004] This application provides a method, apparatus, terminal device, and storage medium for obtaining vehicle passability, which can solve the problem that users cannot determine whether a vehicle can pass through the road conditions shown in the video.

[0005] In a first aspect, embodiments of this application provide a method for obtaining vehicle passability, including:

[0006] Acquire driving video, which includes image information of the first vehicle and image information of the road conditions;

[0007] Based on the driving video, a simulation environment for the driving conditions is constructed;

[0008] Obtain the vehicle simulation model of the second vehicle selected or input by the user;

[0009] Based on the simulation environment and the vehicle simulation model, the simulation results of the second vehicle driving in the simulation environment are obtained, and the simulation results are used to characterize whether the second vehicle can pass through the driving road conditions.

[0010] Secondly, embodiments of this application provide a vehicle passability acquisition device, including:

[0011] The video acquisition module is used to acquire driving video, which includes image information of the first vehicle and image information of the road conditions.

[0012] The environment creation module is used to construct a simulation environment for the driving conditions based on the driving video.

[0013] The model acquisition module is used to acquire the vehicle simulation model of the second vehicle selected or input by the user.

[0014] The simulation test module is used to obtain the simulation results of the second vehicle driving in the simulation environment based on the simulation environment and the vehicle simulation model. The simulation results are used to characterize whether the second vehicle can pass through the driving road conditions.

[0015] Thirdly, embodiments of this application provide a terminal device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the vehicle passability acquisition method described in any one of the first aspects above.

[0016] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the vehicle passability acquisition method described in any one of the first aspects above.

[0017] Fifthly, embodiments of this application provide a computer program product that, when run on a terminal device, causes the terminal device to execute the vehicle passability acquisition method described in any of the first aspects above.

[0018] The beneficial effects of the first aspect of this application compared with the prior art are as follows: This application constructs a simulation environment of driving conditions based on the driving video of the first vehicle, then obtains a vehicle simulation model of the second vehicle, and obtains the simulation results of the second vehicle driving in the simulation environment based on the simulation environment and the vehicle simulation model; This application constructs a simulation environment by using the driving video of the first vehicle, which can reproduce the environment in the driving video, and allows the second vehicle to drive in the simulation environment, which can test the operation of the second vehicle in the simulation environment, determine the passage of the second vehicle through the driving conditions in the driving video, and help users determine whether the second vehicle can drive in the driving conditions in the driving video.

[0019] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1This is a schematic diagram illustrating an application scenario of the vehicle passability acquisition method provided in an embodiment of this application;

[0022] Figure 2 This is a schematic flowchart of a method for obtaining vehicle passability according to an embodiment of this application;

[0023] Figure 3 This is a flowchart illustrating a simulation environment creation method provided in an embodiment of this application;

[0024] Figure 4 This is a flowchart illustrating a method for determining the overall parameters of a first vehicle according to an embodiment of this application.

[0025] Figure 5 This is a flowchart illustrating a simulation environment creation method provided in another embodiment of this application;

[0026] Figure 6 This is a flowchart illustrating a simulation environment creation method provided in another embodiment of this application;

[0027] Figure 7 This is a schematic diagram of the structure of a vehicle passability acquisition device provided in an embodiment of this application;

[0028] Figure 8 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. Detailed Implementation

[0029] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0030] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0031] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0032] Off-road enthusiasts often enjoy challenging terrain. After watching a video of an off-road vehicle traversing challenging terrain, users often want to try it themselves. However, because their vehicle model differs from the one in the video, users cannot determine if their vehicle is suitable for the conditions. Furthermore, the inability to pinpoint the location of the terrain in the video and retrieve it from a map further complicates the decision.

[0033] Based on the above reasons, this application proposes a method for obtaining vehicle passability, which constructs a road condition simulation environment through video, and conducts simulation testing on the vehicle simulation model to be tested in the simulation environment to obtain simulation results. Based on the simulation results, the passability of the vehicle to be tested in the simulation environment can be determined.

[0034] Figure 1 This diagram illustrates an application scenario of the vehicle passability acquisition method provided in this application. The method can be used for vehicle simulation testing to determine the vehicle's passability in road conditions. Specifically, storage device 10 stores driving videos of a first vehicle, and simulation device 20 retrieves the driving videos of the first vehicle from storage device 10, analyzes the retrieved videos, constructs a simulation environment, and performs simulation testing on a second vehicle within the constructed simulation environment to obtain simulation results of the second vehicle driving in the simulation environment.

[0035] Figure 2 A schematic flowchart of the vehicle passability acquisition method provided in this application is shown, with reference to... Figure 2 The method is described in detail below:

[0036] S101, acquire driving video, which includes image information of the first vehicle and image information of the road conditions.

[0037] In this embodiment, the first vehicle can be any type of vehicle. The driving video of the first vehicle can be video captured by a monocular camera or a binocular camera.

[0038] The driving video of the first vehicle can be pre-stored video or video retrieved from external storage devices or servers. The first vehicle can also be a video selected or entered by the user.

[0039] The video of the first vehicle includes the road conditions it travels through, such as ditches, slopes, and mountains.

[0040] S102, Based on the driving video of the first vehicle, construct a simulation environment for the driving conditions.

[0041] In this embodiment, after obtaining the driving video of the first vehicle, a simulation environment for the driving conditions can be constructed based on the driving conditions in the driving video.

[0042] Specifically, the driving video of the first vehicle is input into the environment construction model to obtain the simulation environment of the driving road conditions in the driving video of the first vehicle.

[0043] The simulation environment simulates the road conditions in the driving video of the first vehicle. For example, if the driving video includes trees and mountains, the simulation environment also includes trees and mountains, and the proportion of trees and mountains in the simulation environment is the same as that in the driving video.

[0044] S103, Obtain the vehicle simulation model of the second vehicle.

[0045] In this embodiment, the second vehicle is the vehicle selected or entered by the user.

[0046] In this embodiment, the vehicle simulation model of the second vehicle can be a pre-stored model or a model obtained from an external storage device or server. Specifically, after receiving the first instruction, the electronic device sends a model acquisition request for the vehicle simulation model of the second vehicle to the server. After receiving the model acquisition request, the server sends the vehicle simulation model of the second vehicle to the electronic device. The first instruction can be an instruction input by the user through the human-computer interaction display screen on the electronic device. The model acquisition request includes the model number of the second vehicle.

[0047] The vehicle simulation model of the second vehicle can be constructed using the vehicle's overall parameters, or using images or videos of the second vehicle. The second simulation model can include the vehicle's overall parameters.

[0048] S104, Based on the simulation environment and the vehicle simulation model, obtain the simulation results of the second vehicle driving in the simulation environment.

[0049] In this embodiment, the driving of the second vehicle is simulated and tested in a simulated environment. The simulation results determine the operating condition of the second vehicle in the simulated environment, such as whether the second vehicle can pass through ditches, mountains, jungles, etc. in the simulated environment. The simulation results characterize whether the second vehicle can pass through the driving conditions in the simulated environment. Specifically, the simulation results can indicate whether the second vehicle can pass through the driving conditions in the simulated environment, or whether the second vehicle cannot pass through the driving conditions in the simulated environment.

[0050] In this embodiment, a simulation environment of the road conditions in the driving video of the first vehicle is constructed based on the driving video of the first vehicle. Then, a vehicle simulation model of the second vehicle is obtained. Based on the simulation environment and the vehicle simulation model, the simulation results of the second vehicle driving in the simulation environment are obtained. This application constructs a simulation environment using the driving video of the first vehicle, which can reproduce the environment in the driving video. Driving the second vehicle in the simulation environment can test the operation of the second vehicle in the simulation environment and determine whether the second vehicle can pass through the road conditions in the driving video of the first vehicle. This helps users determine whether the second vehicle can drive through the road conditions in the driving video of the first vehicle. Users do not need to evaluate whether their own vehicle can pass through the road conditions in the driving video of the first vehicle themselves. This application automates the evaluation, and the automated evaluation of this application is more accurate and faster than the user's evaluation.

[0051] like Figure 3 As shown, in one possible implementation, the process of step S102 may include:

[0052] S1021, Based on the image information of the first vehicle in the driving video, determine the overall vehicle parameters of the first vehicle.

[0053] In this embodiment, the overall vehicle parameters of the first vehicle include its dimensions, approach angle, departure angle, maximum wading depth, and passing angle. The dimensions of the first vehicle may include its length, width, and height.

[0054] Specifically, the process involves extracting frame images from the driving video, identifying the frames containing the first vehicle, and extracting the image information of the first vehicle from these frames.

[0055] Specifically, if a frame image includes multiple vehicles, the image information of multiple vehicles will be displayed so that the user can select a vehicle based on the image. The vehicle selected by the user is the first vehicle.

[0056] S1022, Based on the vehicle parameters of the first vehicle and the image information of the driving conditions, construct a simulation environment for the driving conditions.

[0057] In this embodiment, the size of the target object in the driving video can be determined using the vehicle parameters of the first vehicle. The target object can be a ditch, tree, bridge hole, etc.

[0058] Specifically, when the first vehicle passes through a ditch, the depth of the ditch is determined based on the height of the first vehicle and the height of the first vehicle above the water surface.

[0059] When the first vehicle passes through the underpass, the width of the underpass is determined based on the width of the first vehicle.

[0060] The height of the tree is determined based on the height of the first vehicle as it passes the tree.

[0061] In this embodiment, by using two types of data—the overall vehicle parameters of the first vehicle and the image information of the road conditions—multi-source data can make the determined simulation environment more accurate.

[0062] like Figure 4 As shown, in one possible implementation, the process of step S1021 may include:

[0063] S10211, Determine the model of the first vehicle based on the image information of the first vehicle.

[0064] In this embodiment, after obtaining the image information of the first vehicle, the model of the first vehicle is determined by recognizing the image information of the first vehicle.

[0065] S10212, Determine the overall vehicle parameters of the first vehicle based on the model of the first vehicle.

[0066] In this embodiment, the vehicle parameters corresponding to the first vehicle's model number are determined by looking up a table. Alternatively, the electronic device sends the first vehicle's model number to the server, and the server determines the vehicle parameters upon receiving the model number. The server then sends the vehicle parameters to the electronic device.

[0067] Optionally, the driving video is input into the first neural network model to obtain the vehicle parameters of the first vehicle. The first neural network model includes a vehicle determination module and a parameter determination module. The vehicle determination module analyzes the driving video to determine the image information of the first vehicle. The parameter determination module determines the model of the first vehicle based on the image information output by the vehicle determination module; for example, the model of the first vehicle is Tank 300.

[0068] In this embodiment, the overall vehicle parameters of the first vehicle can be accurately determined using the image information of the first vehicle, making the determination of the overall vehicle parameters more accurate and faster. This solves the problem that the user does not know the model of the first vehicle or its overall vehicle parameters.

[0069] like Figure 5 As shown, in one possible implementation, the process of step S1022 may include:

[0070] S10221, Obtain semantic maps and depth maps of multiple images of the driving conditions.

[0071] In this embodiment, image information of the driving road conditions is obtained by processing frame images in the driving video. Alternatively, the image information of the driving road conditions can be frame images of the driving video. Multiple frame images can be consecutive frames from the driving video, or they can be discontinuous frames.

[0072] Semantic segmentation is performed on the image information for each road condition to obtain a semantic map of the image information for each road condition. Semantic segmentation is to classify each pixel in the image, determine the category of each pixel (such as belonging to the background, person, or vehicle, etc.), and then divide the image into regions.

[0073] The image information of the driving conditions is input into the trained second convolutional neural network to obtain a depth map of the driving conditions image information. The depth map, also known as a distance image, refers to an image that uses the distance (depth) from the image acquisition device to each point in the scene as pixel values, and it directly reflects the geometry of the visible surface of the scene.

[0074] Before using the trained second convolutional neural network to obtain the depth map, the initial second convolutional neural network can be trained using frame images from the training set to obtain the trained convolutional neural network.

[0075] Specifically, the camera's location data is determined based on the frame images, and a coordinate system is established based on this location data. The depth map and semantic map of the road condition image information are both determined within this established coordinate system. The camera's location data includes its position and orientation. The camera is the one that acquires frame images.

[0076] S10222, Based on the vehicle parameters of the first vehicle, the semantic map, and the depth map, construct the simulation environment.

[0077] like Figure 6 As shown, specifically, the implementation process of step S10222 may include:

[0078] S201, the semantic map and depth map of the image information of the driving road conditions are fused to obtain the first fused image corresponding to the image information of the driving road conditions.

[0079] In this embodiment, the semantic map and depth map are fused to obtain a first fused image. Image fusion refers to the process of extracting the advantageous information from each channel by using image processing and computer technology to synthesize image data about the same target collected from multiple sources, and finally combining them into a high-quality image.

[0080] It should be noted that during the fusion process, the semantic map and the depth map are the semantic map and depth map of the same image.

[0081] S202, based on the vehicle parameters of the first vehicle, determine the geometric space value of the target object in the first fused image, wherein the geometric space value includes the height and width value of the target object.

[0082] In this embodiment, the target object may include trees, bridges, steep slopes, etc. Geometric spatial values ​​may also include slope values, angle values, etc.

[0083] As an example, the slope of the earthen slope is determined based on the approach angle of the first vehicle.

[0084] S203, the first fused image and the geometric space value of the target object are fused to obtain the second fused image corresponding to the image information of the driving road conditions.

[0085] In this embodiment, fusing the first fused image and the geometric space value of the target object can be done by associating the geometric space value of the target object with the target object, so that the geometric space value of the target object can be determined in the simulation test.

[0086] S204, the various second fused images are stitched together according to the image acquisition order to obtain the simulation environment.

[0087] In this embodiment, the simulation environment is a geometric space environment.

[0088] The image acquisition order of each second fused image is determined based on the order of the image information of each driving road condition in the vehicle driving video.

[0089] The image information or frame images of multiple road conditions have a certain temporal order in the vehicle driving video. Each image information of multiple road conditions corresponds to a second fused image. Therefore, according to the temporal order of the image information of multiple road conditions, the second fused images can be arranged in the order of image acquisition.

[0090] For example, if multiple road condition image information includes A, B, and C, the second fused image corresponding to road condition image information A is A', the second fused image corresponding to road condition image information B is B', and the second fused image corresponding to road condition image information C is C'. If the order of the three road condition image information is A, B, and C, then the image acquisition order of the three second fused images is A', B', and C'.

[0091] In one possible implementation, step S103 may include:

[0092] S1031, Obtain the vehicle parameters of the second vehicle.

[0093] In this embodiment, the overall vehicle parameters of the second vehicle include the dimensions of the second vehicle, approach angle, departure angle, maximum wading depth, and passing angle.

[0094] The overall vehicle parameters of the second vehicle can be determined based on the model number of the second vehicle. Specifically, the model number of the second vehicle selected or input by the user is obtained, and the corresponding overall vehicle parameters are determined by looking up a table.

[0095] The overall vehicle parameters of the second vehicle can also be determined based on an image of the second vehicle selected or entered by the user. Specifically, the model of the second vehicle is determined based on the image, and the overall vehicle parameters are then determined based on the model.

[0096] S1032, Based on the overall vehicle parameters of the second vehicle, construct a vehicle simulation model of the second vehicle.

[0097] In this embodiment, a vehicle simulation model can be constructed based on the overall vehicle parameters of the second vehicle, and the vehicle simulation model can carry the overall vehicle parameters of the second vehicle. Specifically, after obtaining the simulation environment, a vehicle simulation model of the second vehicle can be constructed in the simulation environment based on the overall vehicle parameters of the second vehicle.

[0098] In this embodiment, the vehicle simulation model is constructed using the vehicle parameters of the second vehicle, making the constructed vehicle simulation model more accurate and refined.

[0099] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0100] Corresponding to the vehicle passability acquisition method described in the above embodiments, Figure 7 A structural block diagram of a vehicle passability acquisition device provided in an embodiment of this application is shown. For ease of explanation, only the parts related to the embodiment of this application are shown.

[0101] Reference Figure 7 The device 300 may include: a video acquisition module 310, an environment establishment module 320, a model acquisition module 330, and a simulation testing module 340.

[0102] The video acquisition module 310 is used to acquire driving video, which includes image information of the first vehicle and image information of the road conditions.

[0103] The environment creation module 320 is used to construct a simulation environment of the driving conditions based on the driving video;

[0104] The model acquisition module 330 is used to acquire the vehicle simulation model of the second vehicle selected or input by the user;

[0105] The simulation test module 340 is used to obtain the simulation results of the second vehicle driving in the simulation environment based on the simulation environment and the vehicle simulation model. The simulation results are used to characterize whether the second vehicle can pass through the driving road conditions.

[0106] In one possible implementation, the environment setup module 320 can specifically be used for:

[0107] Based on the image information of the first vehicle in the driving video, the overall vehicle parameters of the first vehicle are determined. The overall vehicle parameters of the first vehicle include the size of the first vehicle, approach angle, departure angle, maximum wading depth and passing angle.

[0108] Based on the vehicle parameters of the first vehicle and the image information of the driving conditions, a simulation environment for the driving conditions is constructed.

[0109] In one possible implementation, the environment setup module 320 can specifically be used for:

[0110] Based on the image information of the first vehicle, determine the model of the first vehicle;

[0111] Based on the model of the first vehicle, determine the overall vehicle parameters of the first vehicle.

[0112] In one possible implementation, the environment setup module 320 can specifically be used for:

[0113] Obtain semantic maps and depth maps of multiple images of the aforementioned road conditions;

[0114] The simulation environment is constructed based on the vehicle parameters of the first vehicle, the semantic map, and the depth map.

[0115] In one possible implementation, the environment setup module 320 can specifically be used for:

[0116] The semantic map and depth map of the image information of the driving road conditions are fused to obtain the first fused image corresponding to the image information of the driving road conditions.

[0117] Based on the vehicle parameters of the first vehicle, the geometric space values ​​of the target objects in the first fused image are determined, and the geometric space values ​​include the height and width values ​​of the target objects;

[0118] The first fused image and the geometric space value of the target object are fused to obtain the second fused image corresponding to the image information of the driving road conditions;

[0119] The simulation environment is obtained by stitching together the various second fused images in the order of image acquisition, wherein the image acquisition order of the various second fused images is determined based on the order of the image information of each driving road condition in the vehicle driving video.

[0120] In one possible implementation, the model acquisition module 330 can specifically be used for:

[0121] Obtain the vehicle parameters of the second vehicle selected or input by the user. The vehicle parameters of the second vehicle include the size of the second vehicle, approach angle, departure angle, maximum wading depth and passing angle.

[0122] Based on the overall vehicle parameters of the second vehicle, a vehicle simulation model of the second vehicle is constructed.

[0123] In one possible implementation, the environment setup module 320 can specifically be used for:

[0124] Semantic segmentation is performed on the image information of multiple driving conditions to obtain semantic maps of the image information of each driving condition.

[0125] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0126] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0127] This application also provides a terminal device, see [link to relevant documentation] Figure 8The terminal device 400 may include: at least one processor 410, a memory 420, and a computer program stored in the memory 420 and executable on the at least one processor 410. When the processor 410 executes the computer program, it implements the steps in any of the above method embodiments, for example... Figure 2 Steps S101 to S104 in the illustrated embodiment. Alternatively, when the processor 410 executes the computer program, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 7 The functions of modules 310 to 340 are shown.

[0128] For example, a computer program may be divided into one or more modules / units, one or more of which are stored in memory 420 and executed by processor 410 to complete this application. The one or more modules / units may be a series of computer program segments capable of performing a specific function, which are used to describe the execution process of the computer program in terminal device 400.

[0129] Those skilled in the art will understand that Figure 8 This is merely an example of a terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than shown, or combine certain components, or different components, such as input / output devices, network access devices, buses, etc.

[0130] The processor 410 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0131] The memory 420 can be an internal storage unit of the terminal device or an external storage device, such as a plug-in hard drive, a smart media card (SMC), a secure digital card (SD), or a flash card. The memory 420 is used to store the computer program and other programs and data required by the terminal device. The memory 420 can also be used to temporarily store data that has been output or will be output.

[0132] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0133] The vehicle passability acquisition method provided in this application embodiment can be applied to terminal devices such as computers, tablets, laptops, netbooks, and personal digital assistants (PDAs). This application embodiment does not impose any restrictions on the specific type of terminal device.

[0134] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0135] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0136] In the embodiments provided in this application, it should be understood that the disclosed terminal devices, apparatuses, and methods can be implemented in other ways. For example, the terminal device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, apparatuses, or units, and may be electrical, mechanical, or other forms.

[0137] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0138] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0139] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by one or more processors, it can implement the steps of the various method embodiments described above.

[0140] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by one or more processors, it can implement the steps of the various method embodiments described above.

[0141] Similarly, as a computer program product, when the computer program product is run on a terminal device, it enables the terminal device to implement the steps in the above-described method embodiments.

[0142] The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media may not include electrical carrier signals and telecommunication signals.

[0143] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for obtaining vehicle passability, characterized in that, include: Acquire driving video, which includes image information of the first vehicle and image information of the road conditions; Based on the driving video, a simulation environment for the driving conditions is constructed, including: determining the overall vehicle parameters of the first vehicle based on the image information of the first vehicle, the overall vehicle parameters including the vehicle's size, approach angle, departure angle, maximum wading depth, and passing angle; performing semantic segmentation on the image information of each driving condition to obtain a semantic map of the image information of each driving condition; the semantic segmentation classifies each pixel in the image to determine the category of each point, thereby dividing the region; inputting the image information of the driving conditions into a trained second convolutional neural network to obtain a depth map of the image information of the driving conditions; fusing the semantic map and the depth map of the image information of the driving conditions to obtain a first fused image corresponding to the image information of the driving conditions; determining the geometric space values ​​of the target objects in the first fused image based on the overall vehicle parameters of the first vehicle, the geometric space values ​​including the height and width values ​​of the target objects; fusing the first fused image and the geometric space values ​​of the target objects to obtain a second fused image corresponding to the image information of the driving conditions; and stitching the various second fused images according to the image acquisition order to obtain the simulation environment. Obtain the vehicle simulation model of the second vehicle selected or input by the user; Based on the simulation environment and the vehicle simulation model, the simulation results of the second vehicle driving in the simulation environment are obtained to determine whether the second vehicle can drive in the road conditions in which the first vehicle is driving; the simulation results are used to characterize whether the second vehicle can pass through the driving road conditions.

2. The method for obtaining vehicle passability as described in claim 1, characterized in that, Determining the overall vehicle parameters based on the image information of the first vehicle includes: Based on the image information of the first vehicle, determine the model of the first vehicle; Based on the model of the first vehicle, determine the overall vehicle parameters of the first vehicle.

3. The method for obtaining vehicle passability as described in claim 1, characterized in that, The process of obtaining the vehicle simulation model of the second vehicle selected or input by the user includes: Obtain the vehicle parameters of the second vehicle selected or input by the user. The vehicle parameters of the second vehicle include the size of the second vehicle, approach angle, departure angle, maximum wading depth and passing angle. Based on the overall vehicle parameters of the second vehicle, a vehicle simulation model of the second vehicle is constructed.

4. A vehicle passability acquisition device, characterized in that, include: The video acquisition module is used to acquire driving video, which includes image information of the first vehicle and image information of the road conditions. An environment establishment module is used to construct a simulation environment for the driving conditions based on the driving video, including: determining the overall vehicle parameters of the first vehicle based on the image information of the first vehicle, the overall vehicle parameters including the size, approach angle, departure angle, maximum wading depth, and passing angle of the first vehicle; performing semantic segmentation on the image information of each driving condition to obtain a semantic map of the image information of each driving condition; the semantic segmentation classifies each pixel in the image to determine the category of each point, thereby dividing the region; inputting the image information of the driving conditions into a trained second convolutional neural network to obtain a depth map of the image information of the driving conditions; fusing the semantic map and the depth map of the image information of the driving conditions to obtain a first fused image corresponding to the image information of the driving conditions; determining the geometric space values ​​of the target objects in the first fused image based on the overall vehicle parameters of the first vehicle, the geometric space values ​​including the height and width values ​​of the target objects; fusing the first fused image and the geometric space values ​​of the target objects to obtain a second fused image corresponding to the image information of the driving conditions; and stitching the various second fused images according to the image acquisition order to obtain the simulation environment. The model acquisition module is used to acquire the vehicle simulation model of the second vehicle selected or input by the user. The simulation testing module is used to obtain the simulation results of the second vehicle driving in the simulation environment based on the simulation environment and the vehicle simulation model, so as to determine whether the second vehicle can drive in the road conditions in which the first vehicle is driving; the simulation results are used to characterize whether the second vehicle can pass through the driving road conditions.

5. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the vehicle passability acquisition method as described in any one of claims 1 to 3.

6. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the vehicle passability acquisition method as described in any one of claims 1 to 3.

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